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17th International Conference on Brain Informatics, BI 2024

作者： Doborjeh, Zohreh lavin, Daniel Hunter, Kirsty Heym, Nadja Heasman, Bryony Doborjeh, Maryam Kasabov, Nikola Gibson, Glen Sumich, Alexander School of Engineering Computer and Mathematical Sciences Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand School of Population Health Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand Department of Sport Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom NTU Psychology School of Social Sciences Nottingham Trent University Nottingham United Kingdom School of Software Engineering Dalian University Dalian China Institute for Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Department of Food and Nutritional Sciences The University of Reading Whiteknights Reading United Kingdom Department of Psychology and Neuroscience Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819632930

The immune and enteric systems have been implicated in psychopathology, including depressive disorders. However, the precise neurocognitive mechanisms remain unclear. The present study uses Brain-Inspired Spiking Neural Network (SNN) technique to model electroencephalographic (EEG) data as a function of gut-microbiota, inflammation, and depressive symptoms. Forty-two participants (nonclinical cohort) were assessed for Bacteroides-Prevotella (BAC) colonization in faecal samples, Interleukin 6 (IL-6) in peripheral blood, depressive symptoms (self-report of cognitive and non-cognitive symptoms) and brain function (resting state EEG). SNN models were applied to EEG data to generate connection weight values across scalp regions. Connection weights were visualized as a function of 1) depression;2) inflammation;3) gut microbiome. Lower depression and higher BAC were associated with higher frontal connection weights (cognitive depression = bilateral, non-cognitive and BAC = left hemisphere only). Lower cognitive depression and lower IL-6 were associated with higher posterior connection weights. The SNN models achieved higher accuracy for theta and alpha EEG sub-bands, highlighting their relevance in differentiating between groups. These findings emphasize the potential of SNNs in predicting parameters from brain functional connectivity data and developing targeted diagnostic and intervention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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Advanced Graph Convolutional Networks for Semantic Relationship Mining in Large-Scale Ontologies

Advanced Graph Convolutional Networks for Semantic Relations...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： R Anuradha Swathi B Rakesh Kumar Amandeep Nagpal Ravi Kalra Hayder Saadoon Abdulaali Institute of Aeronautical Engineering Dundigal Hyderabad Department of Information Science Engineering New Horizon College of Engineering Bangalore Department of Computer Engineering Applications GLA University Mathura Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This research introduces novel methodologies in graph convolutional networks (GCNs) to enhance semantic relationship mining within large-scale ontologies. Traditional approaches to understanding and extracting meaningful patterns from ontological structures often struggle with the complexity and scale of data. The proposed framework leverages advanced GCNs, incorporating a series of innovative mechanisms designed to optimize performance, accuracy, and scalability. Firstly, an enhanced graph convolutional layer is introduced, specifically tailored to capture the nuanced hierarchical and relational data typical of extensive ontological datasets. This layer employs a multidimensional feature extraction technique, significantly improving the depth and quality of semantic relationship identification. Secondly, the paper presents a unique embedding algorithm that efficiently maps semantic and relational data into a lower-dimensional space, facilitating faster computation while retaining critical information. To address the challenges of scalability, a distributed processing architecture is also proposed, enabling the handling of ontologies at an unprecedented scale. Empirical evaluations demonstrate the superiority of the approach in terms of precision, recall, and computational efficiency compared to existing methods. This research contributes to the field by providing a robust tool for experts and practitioners involved in knowledge discovery, data mining, and semantic web technologies, paving the way for more intelligent and efficient ontology management.

关键词： Semantic Web Distributed processing Scalability Semantics computer architecture Ontologies Feature extraction

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Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas. However, extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks. This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle (UAV) to ensure data security at the physical layer. Since such a system requires trade-off policies that balance the secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor critic (TransSAC) algorithm, which is a generative artificial intelligence-enable deep reinforcement learning approach to solve the reformulated problem, so that capturing global dependencies and diversely exploring weights. Simulation results demonstrate that the TransSAC outperforms various baselines, and achieves an optimal secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results find more suitable constraint values for the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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JC5A: Service Delay Minimization for Aerial MEC-assisted Industrial Cyber-Physical Systems

arXiv

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arXiv 2024年

作者： Sun, Geng Wu, Jiaxu Sun, Zemin He, Long Wang, Jiacheng Niyato, Dusit Jamalipour, Abbas Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Electrical and Computer Engineering The University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Auburn University Auburn United States

In the era of the sixth generation (6G) and industrial Internet of Things (IIoT), an industrial cyber-physical system (ICPS) drives the proliferation of sensor devices and computing-intensive tasks. To address the limited resources of IIoT sensor devices, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution, providing flexible and cost-effective services in close proximity of IIoT sensor devices (ISDs). However, leveraging aerial MEC to meet the delay-sensitive and computation-intensive requirements of the ISDs could face several challenges, including the limited communication, computation and caching (3C) resources, stringent offloading requirements for 3C services, and constrained on-board energy of UAVs. To address these issues, we first present a collaborative aerial MEC-assisted ICPS architecture by incorporating the computing capabilities of the macro base station (MBS) and UAVs. We then formulate a service delay minimization optimization problem (SDMOP). Since the SDMOP is proved to be an NP-hard problem, we propose a joint computation offloading, caching, communication resource allocation, computation resource allocation, and UAV trajectory control approach (JC5A). Specifically, JC5A consists of a block successive upper bound minimization method of multipliers (BSUMM) for computation offloading and service caching, a convex optimization-based method for communication and computation resource allocation, and a successive convex approximation (SCA)-based method for UAV trajectory control. Moreover, we theoretically prove the convergence and polynomial complexity of JC5A. Simulation results demonstrate that the proposed approach can achieve superior system performance compared to the benchmark approaches and algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue Generation

arXiv

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arXiv 2023年

作者： Liu, Lei Huang, Jimmy Xiangji Information Retrieval and Knowledge Management Research Lab Department of Electrical Engineering & Computer Science York University Toronto Canada Information Retrieval and Knowledge Management Research Lab School of Information Technology York University Toronto Canada

Dialogue systems for non-English languages have long been under-explored. In this paper, we take the first step to investigate few-shot cross-lingual transfer learning (FS-XLT) and multitask learning (MTL) in the context of open-domain dialogue generation for non-English languages with limited data. We observed catastrophic forgetting in both FS-XLT and MTL for all 6 languages in our preliminary experiments. To mitigate the issue, we propose a simple yet effective prompt learning approach that can preserve the multilinguality of multilingual pre-trained language model (mPLM) in FS-XLT and MTL by bridging the gap between pre-training and fine-tuning with Fixed-prompt LM Tuning and our hand-crafted prompts. Experimental results on all 6 languages in terms of both automatic and human evaluations demonstrate the effectiveness of our approach. Our code is available at https://***/JeremyLeiLiu/XLinguDial. Copyright © 2023, The Authors. All rights reserved.

关键词： Linearization

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An Effective Analysis of Cloud Related Platform using Big Data Approaches for Effective Implementation of High-Performance Computing through Structural Equation Modeling (SEM) Approach 2

An Effective Analysis of Cloud Related Platform using Big Da...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Bansal, Gourav Saxena, Monika Johar, Siti Sarawati Othman, Bestoon Abdulmaged Husain, S.Syed Kapila, Dhiraj Information Technology Kurukshetra University India Computer Science Banasthali Vidyapith Rajasthan India Centre for General Studies and Co-curricular Universiti Tu Hussein Onn Malaysia Department of Social Science Malaysia College of Administration & Financial Sciences Knowledge University- Kurdistan Region Iraq. Business Administration Koya Technical Institute Erbil Polytechnic University Erbil Iraq K.Ramakrishnan College of Engineering Department of ECE Trichy India Lovely Professional University Department of Computer Science & Engineering Phagwara India

ISBN: (纸本)9781665437899

The recent technologies are focusing on High- performance computing to fasten the complex calculations and market procedures in industries. Therefore, analysis of the big- data and cloud-computing is necessary to implement the 'High- performance computing' (HPC) in practice. Based on the advantages, the research aimed to identify the advantages and disadvantages of cloud computing as well as HPCs. The research used SEM approach to justify the procedure for implementing HPC effectively. In this research, SPSS software was used to conduct multiple regression analysis. A significance value of p © 2022 IEEE.

关键词： Big data

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Optimized Energy-Efficient Hybrid LW-AES and LW-ECC Security Framework for VLC and RF Networks

Optimized Energy-Efficient Hybrid LW-AES and LW-ECC Security...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Venkatesan C Thamaraimanalan T Sathiya T Ranjithkumar M Radha K Shanmuga Priya K Department of Electronics and Communication Engineering HKBK College of Engineering Bengaluru Karnataka India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu India Department of Computer Science and Engineering Sona College of Technology Salem Tamilnadu India Department of Computer Science and Business Systems Knowledge Institute of Technology Salem Tamilnadu India Department of AI & DS Vivekananda College of Technology for Women Thiruchengode Tamilnadu India Department of Cyber Scurity K.S.R College of Engineering Thiruchengode Tamilnadu India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

In hybrid Visible Light Communication (VLC) and Radio Frequency (RF) networks, optimizing energy efficiency and ensuring secure resource allocation are key challenges. This paper addresses these issues by introducing a Hybrid Lightweight Advanced Encryption Standard (LW-AES) and Lightweight Elliptic Curve Cryptography (LW-ECC) secure resource algorithm. This innovative approach aims to enhance the sustainability and reliability of communication systems across diverse environments. Our analysis reveals substantial performance improvements compared to existing methods, including higher throughput (100 Mbps) and packet delivery ratio (98%), with reduced latency (10 ms) and extended network lifetime (500 hours). The algorithm also features lower memory usage (100 KB) and enhanced energy efficiency, showing a 20% decrease in energy consumption (800 mW) and an energy efficiency of 0.8 mJ/bit. Security is bolstered with a 25% reduction in interception probability (3.75%) and a 50% increase in secure communication range (150 meters). Moreover, the approach aligns with green communication objectives, reducing CO2 emissions by 20% (800 kg/year) and achieving an energy efficiency metric of 10 nJ/bit. Communication safety is markedly improved, with an 80% decrease in outage probability (1%) and a 40% boost in reliability (84%). By integrating LW-AES and LW-ECC in a hybrid configuration, this research offers a robust framework for advancing sustainable and resilient hybrid VLC-RF networks, effectively meeting the evolving needs of modern communication systems.

关键词： Radio frequency Communication systems Memory management Throughput Energy efficiency Safety Security Resource management Reliability Visible light communication

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Fuzzy Preference Completion with Ranked and Unranked Preferences

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Cognitive Computation 2025年第3期17卷 1-21页

作者： Li, Lei Liu, Pan Zhang, Renjie Tao, Zhenchao Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Radiation Oncology The First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei China Department of Radiation Oncology Anhui Provincial Cancer Hospital Hefei China

As for social choice, all alternatives are ranked by agents to form preferences as linear orders. However, in applications, sometimes some alternatives cannot be ranked, or it is unnecessary to rank them, which leads to unranked alternatives. Hence, without loss of generality, by dividing the set of alternatives into three ranked and unranked subsets, including top-k alternatives, intermediate-r alternatives, and last-l alternatives, the Mallows model on ranked and unranked preferences can be analyzed systematically. Technically, a repeated insertion model is adopted during sampling, and probability distributions are derived for ranked and unranked preferences of alternatives. Experimental results verify the accuracy of the probability distributions for different ranked and unranked preferences of alternatives. Furthermore, in order to solve the preference completion problem where agents have multiple partial rankings, a fuzzy preference completion algorithm, Fuzzy-Multi-Rankings, is proposed, which introduces a fuzzy ranking to complete the target agent’s preference in addition to the traditional nearest-neighbor-based methods. Based on the three ranked and unranked preferences, seven cases can be classified and analyzed for fuzzy preference completion. Experiments on the synthetic datasets and MovieLens dataset confirm the effectiveness and efficiency of our proposed Fuzzy-Multi-Rankings algorithm and also verify the accuracy of the evaluated probability distributions for the proposed seven cases.

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Predicting Cyber Attacks on Integrated Energy and Gasoline Resources

Predicting Cyber Attacks on Integrated Energy and Gasoline R...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Jasim Ahmede Jasim Aaied Eqab Muraay Majed Mina Ismail Mahmoud Waleed Al-Azzawi Mustafa Zuhaer Nayef Al-Dabagh Al-Nisour University College Baghdad Iraq Medical Engineering Al Kitab University Kirkuk Iraq Department of Radiology Techniques University: Uruk University Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Department of Computer Science College of Science Knowledge University Erbil Iraq

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Connecting gas and electricity networks has recently been proposed as a viable solution to manage the unpredictability of renewable energy sources and increase the versatility of power operations. When critical operational communication and control inputs from both systems must be shared, the likelihood of cybercrime increases. This study introduces a novel approach, the Energy and Gasoline Integration for Cyber-Attacks (EGICA) model, which addresses cybersecurity in interconnected gas and electricity networks—a solution to manage renewable energy unpredictability and enhance operational flexibility. The model illustrates how undetected cyberattacks can compromise network operations by using power-to-gas (PtG) and gas-to-generation (GtG) methods. Two innovative detection techniques are proposed for identifying hazardous false data in PtG/GtG schedulers. The first employs fully convolutional and wavelet transformations to detect attacks in facilities planning data. In contrast, the second utilizes a hybrid neuron with unsupervised learning to detect attacks on scheduler outputs without labelled training data. These methods support a robust cyber-attack scheduling framework within integrated gas and electricity networks. Simulation tests on a bus energy system connected to a utility grid validate the model’s effectiveness using historical data for evaluation. Results show that EGICA achieves high accuracy (86.3%), precision (89.1%), recall (87.2%), F-measure (90.2%), and efficiency (78.7%) compared to other methods, confirming its reliability in detecting cyber threats in complex energy networks.

关键词： Wavelet transforms Renewable energy sources Scientific computing Electricity Supervised learning Training data Cyberattack Petroleum Unsupervised learning Testing

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Next-Generation Computational Models for Enhanced Interpretation of Biospeckle Images Leveraging Deep Learning and Fourier Transform Techniques in Biomedical Applications

Next-Generation Computational Models for Enhanced Interpreta...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： R Gowtham Raj S Sreekanth Ginni Nijhawan Shubhi Gupta Saurabh Sharma T Mounika Deparment of Mechanical Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Plot No. 11 Knowledge Park II Greater Lloyd Law College Noida Uttar Pradesh India Lovely Professional University Phagwara India Amity School of Engineering & Technology Amity University Greater Noida India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study looks at advanced computational techniques that enhance the understanding of biospeckle images in biological contexts. Our models use Fourier transform and deep learning techniques to achieve improved specificity, recall, accuracy, and precision. The Fourier transform may provide a more accurate description of frequency domain data as biological processes evolve over time. Deep learning algorithms recognise patterns in biospeckle images, facilitating analysis, memory, and response. The analysis research delineates the several components that influence the outcome and provides evidence of their respective roles. This research shows how helpful the models are and offers suggestions on how to make them better. Non-invasive biomedical testing uses these computer models to illustrate the temporal evolution of biological cells. Our techniques improve the interpretation of biospeckle images, potentially revolutionising non-invasive biomedical imaging. Future researchers may evaluate these models using different datasets and testing conditions. This guarantees their adaptability and power in biological contexts.

关键词： Deep learning Adaptation models Fourier transforms Accuracy Evolution (biology) Computational modeling Biological system modeling Frequency-domain analysis Biomedical imaging Testing

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